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应用(非)刚性图像配准对正电子发射断层扫描反应评估中重复使用基线感兴趣区的影响。

Effects of reusing baseline volumes of interest by applying (non-)rigid image registration on positron emission tomography response assessments.

机构信息

Department of Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, Noord-Holland, The Netherlands.

Exploratory Clinical and Translational Research, Bristol-Myers Squibb, Princeton, New Jersey, United States of America.

出版信息

PLoS One. 2014 Jan 28;9(1):e87167. doi: 10.1371/journal.pone.0087167. eCollection 2014.

Abstract

OBJECTIVES

Reusing baseline volumes of interest (VOI) by applying non-rigid and to some extent (local) rigid image registration showed good test-retest variability similar to delineating VOI on both scans individually. The aim of the present study was to compare response assessments and classifications based on various types of image registration with those based on (semi)-automatic tumour delineation.

METHODS

Baseline (n = 13), early (n = 12) and late (n = 9) response (after one and three cycles of treatment, respectively) whole body [(18)F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (PET/CT) scans were acquired in subjects with advanced gastrointestinal malignancies. Lesions were identified for early and late response scans. VOI were drawn independently on all scans using an adaptive 50% threshold method (A50). In addition, various types of (non-)rigid image registration were applied to PET and/or CT images, after which baseline VOI were projected onto response scans. Response was classified using PET Response Criteria in Solid Tumors for maximum standardized uptake value (SUV(max)), average SUV (SUV(mean)), peak SUV (SUV(peak)), metabolically active tumour volume (MATV), total lesion glycolysis (TLG) and the area under a cumulative SUV-volume histogram curve (AUC).

RESULTS

Non-rigid PET-based registration and non-rigid CT-based registration followed by non-rigid PET-based registration (CTPET) did not show differences in response classifications compared to A50 for SUV(max) and SUV(peak), however, differences were observed for MATV, SUV(mean), TLG and AUC. For the latter, these registrations demonstrated a poorer performance for small lung lesions (<2.8 ml), whereas A50 showed a poorer performance when another area with high uptake was close to the target lesion. All methods were affected by lesions with very heterogeneous tracer uptake.

CONCLUSIONS

Non-rigid PET- and CTPET-based image registrations may be used to classify response based on SUV(max) and SUV(peak). For other quantitative measures future studies should assess which method is valid for response evaluations by correlating with survival data.

摘要

目的

通过应用非刚性和在一定程度上(局部)刚性图像配准来重复使用基线感兴趣区(VOI),其测试-重测变异性与分别在两次扫描上勾画 VOI 相似。本研究旨在比较基于各种类型的图像配准的反应评估和分类与基于(半自动)肿瘤勾画的反应评估和分类。

方法

在患有晚期胃肠道恶性肿瘤的患者中,采集了基线(n=13)、早期(n=12)和晚期(分别在治疗后 1 个和 3 个周期时)全身[(18)F]氟代-2-脱氧-D-葡萄糖正电子发射断层扫描/计算机断层扫描(PET/CT)扫描。为早期和晚期反应扫描识别了病变。使用自适应 50%阈值方法(A50)在所有扫描上独立绘制 VOI。此外,将各种类型的(非)刚性图像配准应用于 PET 和/或 CT 图像,然后将基线 VOI 投影到反应扫描上。使用实体瘤 PET 反应标准最大标准化摄取值(SUV(max))、平均 SUV(SUV(mean))、峰值 SUV(SUV(peak))、代谢活跃肿瘤体积(MATV)、总病变糖酵解(TLG)和累积 SUV-体积直方图曲线下面积(AUC)来对反应进行分类。

结果

与 A50 相比,基于非刚性 PET 的配准和基于非刚性 CT 的配准随后进行基于非刚性 PET 的配准(CTPET)对于 SUV(max)和 SUV(peak)的反应分类没有差异,然而,对于 MATV、SUV(mean)、TLG 和 AUC 有差异。对于后两者,这些配准在小肺病变(<2.8 ml)时表现较差,而 A50 在目标病变附近存在高摄取区域时表现较差。所有方法都受到具有非常不均匀示踪剂摄取的病变的影响。

结论

基于非刚性 PET 和 CTPET 的图像配准可用于基于 SUV(max)和 SUV(peak)对反应进行分类。对于其他定量指标,未来的研究应通过与生存数据相关联来评估哪种方法对反应评估有效。

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